Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

RESEARCH ARTICLE OPEN ACCESS

Kokum Fruit Bar Development via Response Surface Methodology (RSM).

Pritam Bafna*, Mrs. N. Manimehalai Fruit and Vegetable Laboratory, Department of Food and Process Engineering, SRM University, Kattankulathur,

Abstract A response surface methodology (RSM) was used for the determination of optimum ingredients level to prepare kokum fruit bar. Kokum pulp was extracted using water extraction method at temperature (24.97ºC) and time (30.42 min). The effects of ingredients levels on sensory parameters like texture, overall acceptability and calcium content of the prepared fruit bar were studied by employing a Box- Behnken Design (BBD). During the experimental trials the pulp quantity is kept fixed. The coefficient of determination R² for texture, overall acceptability and calcium content were 0.8298, 0.9239 and 0.9842 respectively. Analysis of variable (ANOVA) performed on the experimental values showed that sugar and milk powder were the most important factors that affected characteristics of the kokum fruit bar as it exerted a highly significant influence (p < 0.05) on all the dependent variables. Based on surface and contour plots, optimum ingredients level for formation of kokum fruit bar were pulp, sugar, milk powder; 50g, 40g and 9.39g respectively. Key words: Kokum, Fruit Bar, Response Surface Methodology, Box- Behnken Design, Calcium, Optimization

I. Introduction digestive drinks. Both birindi saar and kokum kadi indica Choisy belonging to the are supposed to be digestive and to relieve gastric family Guttiferae (in the ) is an problems. As kokum is a highly under-utilized fruit, indigenous of India. It was originally found only it can be preserved by making it as a processed food in the western peninsular coastal regions and the to use it during off-season. (Krishnamurthy, adjoining in the states of , Ravindranath, B. 1982) , and Kerala, India as well as parts of Eastern India in the states of West Bengal, Assam 1.2 Objective of Research: and North Eastern Hill regions. Studies have shown Perishable nature of kokum and that the rind contains moisture (80.0 g/100 g), protein unavailability of the preserved product in the market (1%), tannin (1.7%), pectin (0.9%), Total sugars gave rise to this research. This study was planned (4.1%) and fat (1.4%). The seed is very rich in keeping in view the nutritional importance of kokum stearic, oleic and stearic triglycerides Phytochemical and to utilize it by preserving as Fruit bar. This studies have shown that when compared with any kokum fruit bar provides both nutritional and other natural sources, kokum rind contains the phytochemical benefits to human population. Also its highest concentration of anthocyanins (2.4 g/100 g of industrial application will help to catch worldwide kokum fruit) (Nayak, Rastogiet al., 2010;Nayak, consumers of kokum. Srinivas, et al., 2010). The anthocyanins cyanidin-3- glucoside and cyanidin-3-sambubioside are the major II. Materials and Methods pigment present in kokum and is reported to occur in 2.1 Collection of Fruits the ratio of 4:1. Studies have shown that (-)- rubbed kokum rinds (completely blackish red in hydroxycitric acid (HCA) is the major organic acid in colour) sugar and powder were purchased kokum leaves and rinds. from a local shop in Parrys Market, Chennai. In summer the ripe rinds are ground in a blender with sugar and and consumed as a 2.2 Methodology cooling drink. Addition of kokum is supposed to Fruit bar was prepared by drying the boiled enhance the taste of coconut-based curries and to kokum pulp with addition of sufficient quality of remove the unpleasant smell of mackerel and sugar and other ingredients. The process involves in sardines. They are also used in some vegetable dishes fruit bar preparation is shown in Fig 1. and to prepare chutneys and pickles. The Goans regularly prepare kokum kadi or birinda solkadhi. This curry is used with rice or like an after meal

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Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

Formation of kokum pulp samples was used for each treatment. Equal amount (100ml) of water was added to the washed rinds. The washed rinds were soaked in water at 24.97°C temperature for 30.42 min of time. The kokum pulp Preparation of sugar syrup (65°brix) was extracted by grinding into the mixer operated at 5000 rpm for 5 min at controlled room temperature (28-32 °C). After extracted, the pulp was strained Addition of pulp to sugar syrup through 20 mesh stainless steel sieve to get the smooth pulp.

2.3 Experimental Design Layout Addition of milk powder, Liquid glucose The levels of ingredients were selected based on basic trials. By keeping the pulp quantity constant as 50gmthe independent variables Heating the mixture (TSS 75°brix) considered were: Sugar quantity (40-60g) and milk powder quantity (5.0-15.0g). A two -variable (three levels of each variable) 3-level factorial design and a Pouring the mixture into aluminium greased tray response surface methodology (RSM) were used to understand the interactions of sugar and milk powder quantity on the texture overall acceptability and Drying into a tray drier (at 50°C, 62 hrs) calcium content of the prepared fruit bar in 13 runs (Table2.2), of which 4 were centre edge, four were factorial, and five were at centre point.

Cutting into bar shapes (3 cm X 5 cm) Table 2.1 - Processing ingredients and their levels Ingredients Levels Sugar (g) 40, 50, 60 Packed into LDPE aluminium covers Milk powder (g) 5, 10, 15

Fig 1.Process of making Kokum Fruit Bar. 2.4 Fruit Bar Development Trials

The fruit bar development trials were carried out and 2.2.1 Equipment used the responses observed also shown in the table 2.2 The laboratory scale mixer consists of two 2.4Determination of Sensory Parameters parts- Mixing jar and motor (Preethi Himaachal& Co. The sensory parameters of the prepared fruit Solan, India). A grinding blade made up of stainless bar samples like texture and overall acceptability steel (1.6 mm thick) with three cutting edges was were evaluated on the basis of 9 point hedonic scale used to grind the material in the jar. using B2 Monadic Test of sensory. The samples were 2.2.2 Extraction of Kokum Pulp given to 20 semi trained panel members. Purchased kokum rinds were washed in cold water three times for the removal of salt.100 g of the Table 2.2 - Effect of process variables on texture, overall acceptability and calcium content of prepared kokum fruit bar.

Factor 1 Factor 2 Response 1 Response 2 Response 3 Sugar (g) Milk powder Texture Overall Ca content X (x ) (g) Acceptability (mg/100g) Run X2 (x2) (OA) 1 40 (-1) 10 (0) 8.52 8.69 94 2 50 (0) 10 (0) 8.34 8.5 92 3 60 (+1) 10 (0) 8.17 8.27 93 4 50 (0) 10 (0) 8.32 8.43 92 5 50 (0) 10 (0) 8.3 8.5 93 6 40 (-1) 5 (-1) 8.1 8.32 84 7 60 (+1) 5 (-1) 7.27 7.86 85 8 60 (+1) 15 (+1) 8.36 8.38 105 9 50 (0) 10 (0) 8.37 8.41 92 www.ijera.com 224 | P a g e

Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

10 50 (0) 10 (0) 8.28 8.37 93 11 40 (-1) 15 (+1) 7.85 8.46 104 12 50 (0) 15 (+1) 8.47 8.23 105 13 50 (0) 5 (-1) 8.3 8.12 84 Liquid glucose = 2g x represents the coded level of variables; X represents the actual level of variables.

2.5 Calcium Content Determination 2.7Optimization Calcium is determined as calcium pectate. Numerical and graphical optimization was Pectin extracted from material is saponified carried out for the independent variables to obtain the with alkali and precipitated as calcium pectate from fruit bar with Maximum texture, Overall acceptability an acid solution by addition of calcium chloride. The scores and calcium content using Design-Expert calcium pectate precipitate is washed until free from software. Conventional graphical method was applied chloride, dried and weighed. to obtain Maximum pulp of all responses. Predictive models were used to graphically represent systems. 2.6 Statistical Analysis for Optimization of Contour plots of the response variables were utilized Ingredients Quantity to select optimum ingredients level of kokum pulp The response functions y (dependent sugar, milk powder quantity for the production of variables) were sugar quantity and milk powder kokum fruit bar. quantity. The following second-order polynomial model was fitted to the dependent variables with the III. Results and Discussions experimental data (Eq.1.1) 3.1 Statistical Analysis 2 2 (1.1) y =b0 + b1 X1 + b2 X2+ b12 X12 b12 X1 b22 X2 Table 3.1, summarizes the results of each dependent variable with their coefficients of

determination (R²). The statistical analysis indicates The coefficients of the polynomial were that the proposed model was adequate, possessing no represented by b (constant term), b and b (linear 0 1 2 significant lack of fit and with very satisfactory effects), b and b (quadratic effects), and b 11 22 12 values of the R² for all the responses. The R² values (interaction effects).The analysis of variance for texture, overall acceptability and calcium content (ANOVA) tables were generated and the effect and were 0.8298, 0.9239 and 0.9842 respectively. The regression coefficients of individual linear, quadratic closer the value of R² to unity, the better is the and interaction terms were determined. The empirical models fit the actual data. On the other significance of all the terms in the polynomial was hand, the smaller the value of R² the less relevance judged by computing the P value (Prob.> F) at 0.001, the dependent variables in the model have in 0.01 or0.05 significance level. Response surfaces and explaining the behaviour of variations (Little & Hills, contour plots were generated with the help of 1978; Mendenhall, 1975). commercial statistical package, Design-Expert (2010) — version 8.1.6 STAT-EASE, USA. Table 3.1 Regression coefficients and ANOVA of the second-order polynomial model for the response variables texture, OA and calcium content (in coded units). Run df Estimated Variables F- values P value Prob. > F Texture OA Ca Texture OA Ca Texture OA Ca cont cont cont Model 5 8.39 8.44 92.55 6.83 17.01 238.88 0.0128 0.0009 0.0001 X₁ 1 -0.11 -0.16 0.17 2.39 28.35 0.32 0.1661 0.0011** 0.5920 X₂ 1 0.17 0.13 10.17 5.43 18.24 1172.92 0.0526 0.0037** 0.0001*** X₁ X₂ 1 0.34 0.095 0.001 14.33 6.66 0.001 0.0068 * 0.0364* 0.9875 X₁² 1 -0.22 0.058 0.57 4.45 1.73 1.69 0.0728 0.2297 0.23465 X₂² 1 -0.18 -0.25 1.57 3.01 31.03 12.86 0.01256 0.0008*** 0.0089** Lack-of-fit 0.0786 0.1954 0.1743 R² 0.8298 0.9239 0.9842 Pred. R² 0.7883 0.8696 0.9616 ***Significant at 0.001 level; **Significant at 0.01 level; *Significant at 0.05 level 3.2 Effect of Ingredients on Response Variables 3.2.1 Texture

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Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

ANOVA (Table 3.1) indicated that the texture was highly significant at 1% level on Where, X1is the sugar quantity in grams; interaction term of Sugar and milk powder quantity. X2is the milk powder quantity. The interaction term Quadratic terms of process variables were not having of pulp quantity, (Eq. 1.2) indicated that colour any significant effect. By neglecting the non increased with increase of this variable. The significant terms in Eq. 1.1and with the coded values quadratic terms suggested that increase of these of independent variables, the following equation (Eq. variables resulted in increase of colour. The variation 1.2) describes the effect of significant process of colour with pulp, sugar level were graphically variables on texture of the produced fruit bar. presented in the 3-D surface plot and contour plots 2 (1.2) Texture = 8.39 + 0.34 * X1X2(R = 0.82) (Fig. 3.1a, 3.2b).

Fig. 3.1a 3-D Response surface plots Fig. 3.2b Contour plots

Showing the effect of sugar, milk powder quantity on texture of fruit bar

3.2.2Overall Acceptability Interaction term also had the significant effect of 5% ANOVA (Table 3.1) indicated that the level on overall acceptability. By neglecting the non Anthocyanin content was significant at 1% level on significant terms in Eq. 1.1and with the coded values linear terms of sugar quantity and milk powder of independent variables, the following equation (Eq. quantity. Quadratic terms of process variable; milk 1.3) describes the effect of significant process powder quantity was having significant effect at variables on overall acceptability of the produced 0.1% level on overall acceptability of the fruit bar. fruit bar.

2 2 (1.3) O.A. = 8.44 - 0.16 * X1+ 0.13 * X2 + 0.095 * X1X2 – 0.25 * X2 (R = 0.92)

The positive coefficients of the linear term 3.2.3 Calcium Content of milk powder quantity (Eq. 1.3) indicated that ANOVA (Table 3.1) indicated that the overall acceptability content increased with increase calcium content was significant at 0.1% level on of milk powder. The quadratic terms suggested that linear term of milk powder quantity. Quadratic terms increase of this variable resulted in decrease of of process variable; milk powder quantity was having overall acceptability. The interaction term showed significant effect at 5% level on calcium content. No that increase in the term causes increase of overall Interaction term also had any significant effect. By acceptability. The variation of Overall acceptability neglecting the non significant terms in Eq. 1.1and with sugar and milk powder were graphically with the coded values of independent variables, the presented in the 3-D surface plot and contour plots following equation (Eq. 1.4) describes the effect of (Fig. 3.3a, 3.4b). significant process variables on calcium content of the produced fruit bar.

2 2 (1.4) Calcium content = 92.55 +10.17 * X2 + 1.57 * X2 (R = 0.98)

The positive coefficients of the linear term content increased with increase of this variable. The of process variables (Eq. 1.4) indicated that calcium quadratic terms suggested that increase of milk

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Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230 powder resulted in increase of calcium content. The powder were graphically presented in the 3-D surface variation of calcium content with sugar and milk plot and contour plots (Fig. 3.5a, 3.6b)

Fig. 3.3a 3-D Response surface plots Fig. 3.4b Contour plots Showing the effect of sugar, milk powder quantity on overall acceptability of fruit bar

Fig. 3.5a 3-D Response surface plots Fig. 3.6b Contour plots Showing the effect of sugar, milk powder quantity on calcium content of fruit bar

3.3 Numerical Optimization Figs. 3.7 – 3.9show the optimum levels of the process parameters to obtain the fruit bar with maximum texture, overall acceptability score and calcium content, respectively by numerical optimization. The contours indicate that when the fruit bar was prepared with pulp, sugar, milk powder; 50g, 40g and 9.39g respectively gives the predicted value of maximum texture score 8.29, overall acceptability score 8.68 . and calcium content 91.73mg/100g.

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Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

Fig. 3.7 Contour plot for Texture Fig.3.8 Contour plot for overall acceptability

Fig. 3.9 Contour plot for calcium content

3.4 Optimized Ingredients Level for Kokum Fruit optimum was obtained at pulp, sugar, milk powder; Bar Production 50g, 40g and 9.39g respectively from the overlay In graphical optimization overlay plot (Fig. plot. 3.10) was obtained by applying superimposing surface methodology to contour plots of the response variables to select optimum levels of ingredients. The

Fig. 3.10 Optimium ingredients level for kokum fruit bar poroduction

www.ijera.com 228 | P a g e Fig. 3.10 Optimium ingredients level for kokum fruit bar poroduction

Pritam Bafna et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.223-230

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